DTI Quality Control - Part 4: Further reading

Wednesday, April 02, 2014
Do Tromp
0
Comments

Analysis of diffusion images are complicated by the fact that errors can be easily hidden inside the calculated tensor data and not clearly show up in your final processed FA image. Quality control is thus an essential step to ensure data quality. Three previous blog post were dedicated to quality control after acquisition, during tensor calculation and suggested tools to help with QC. This post will suggest some articles for further reading, mainly focussed on best practice during quality control.

To start off I would like to suggest this excellent article by Tournier, Mori and Leemans.In this publication the authors discuss a number of issues that you can pay attention to. Like the examples from figure 1 in their article. There are more examples in their full article that you can check out.

" In order to encourage the use of improved DW-MRI methods, which have a better chance of characterizing the actual fiber structure of white matter, and to warn against the misuse and misinterpretation of DTI, we review the physics of DW-MRI, indicate currently preferred methodology, and explain the limits of interpretation of its results. We conclude with a list of ‘Do's and Don'ts’ which define good practice in this expanding area of imaging neuroscience."

A recent paper by a german neurosurgery group gives a comprehensive overview of software packages that can be used for fiber tracking and compares their efficacy running different fiber tracking algorithms. Results are reviewed by a physicist, neuroradiologist and neurosurgeon (lower scores are better):

It is clearly not easy to acquire diffusion images without error, the successful acquisition of diffusion imaging data in neonates is a factor more complicated. This article give some important tips for acquisition guidelines - for example note that gray/white matter contrast is not nearly as pronounced yet, and sometimes looks opposite in brightness as compared to older populations. This is due to reduced myelination of the white matter at this stage, and can be countered by changing the relaxation time of the T1 or T2 scan. They also go into details of effective quality assessment, to catch issues arising due to movement, pulsation or reduced head size.

Finally, a group at Vanderbilt suggest a number of complementary metrics that can be used for quality assurance and compare their unified pipeline with visual inspection and show that their method can produce a 70% time saving. More information on their methods in their article.

About This Website

Diffusion-imaging.comgives a comprehensive overview of available software, analyzing methods and research possibilities. Providing background on the tools, methods and software to effectively analyze DTI data. Posts include step-by-step processing and tractography tutorials.

About the developer

Do grew up and went to college in the Netherlands, where she also received her masters degree in Neuroscience & Cognition. She is currently a neuroscience graduate student in the US. Her interest is focused on understanding the neural substrates that underlie normal and abnormal brain functioning. She uses state of the art imaging methods, like magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI) to examine alterations in white-matter structure.

Copyright and Licence

The code on this website is licensed under the GNU 3.0 open source license and you are free to modify and redistribute the code, given that you give others you share the code with the same right, and cite this website and the name of the author.

By reading on you agree to these terms.

For more citable references to the posts on this website visit The Winnower.